Prepare county level data
Overview of time windows
US prevalence: 03/09 - 04/18 US socdist: 03/01 - 05/03
UK prevalence: 03/09 - 04/10 UK socdist: 03/01 - 03/31
GER prevalence: 01/01 - 04/25 GER socdist: 02/25 - 04/27
Conty level controls
df_us_ctrl <- read.csv('controls_US.csv')
df_us_ctrl <- df_us_ctrl %>% select(-county_name) %>%
rename(county_fips = county)
df_us_ctrl %>% head()
NA
Social distancing data unacast
df_us_socdist <- read_csv('0409_sds-full-county.csv')
# create sequence of dates
date_sequence <- seq.Date(as.Date('2020-03-09'),
as.Date('2020-03-31'), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge day index with gps data
df_us_socdist = df_us_socdist %>%
merge(df_dates, by='date') %>%
arrange(county_fips) %>%
as_tibble()
df_us_socdist %>% head()
Social distancing data FB
fb_files <- list.files('../FB Data/US individual files/Mobility/',
'*.csv', full.names = T)
df_us_socdist_fb <- fb_files %>%
map(read_csv) %>% bind_rows()
df_us_socdist_fb$ds %>% summary()
Min. 1st Qu. Median Mean 3rd Qu. Max.
"2020-03-01" "2020-03-16" "2020-04-01" "2020-04-01" "2020-04-17" "2020-05-03"
df_us_socdist_fb <- df_us_socdist_fb %>%
select(-age_bracket, -gender, -baseline_name, -baseline_type) %>%
rename(date = ds,
county_fips = polygon_id,
county_name = polygon_name,
socdist_tiles = all_day_bing_tiles_visited_relative_change,
socdist_single_tile = all_day_ratio_single_tile_users)
df_us_socdist_fb <- df_us_socdist_fb %>%
filter(date >= '2020-03-09' & date <= '2020-03-31') %>%
group_by(county_fips) %>%
arrange(date) %>%
mutate(time = row_number()) %>%
ungroup() %>%
arrange(county_fips)
head(df_us_socdist_fb)
Sanity check socdist data
socdist <- df_us_socdist %>% merge(df_us_socdist_fb, by = c("county_fips", "time"))
socdist[c('daily_distance_diff', 'daily_visitation_diff', 'socdist_tiles', 'socdist_single_tile')] %>%
cor(use = 'pairwise.complete')
daily_distance_diff daily_visitation_diff socdist_tiles socdist_single_tile
daily_distance_diff 1.0000000 0.1361318 0.3061683 -0.2746350
daily_visitation_diff 0.1361318 1.0000000 0.3826102 -0.3624062
socdist_tiles 0.3061683 0.3826102 1.0000000 -0.7544123
socdist_single_tile -0.2746350 -0.3624062 -0.7544123 1.0000000
Merge data
df_us <- plyr::join_all(list(df_us_covid, df_us_socdist_fb),
by = c('county_fips', 'time'),
type = 'inner') %>%
plyr::join(df_us_ctrl, by='county_fips') %>%
arrange(county_fips, time)
# keep only counties with full data
fips_complete <- df_us %>%
group_by(county_fips) %>%
summarize(n = n()) %>%
filter(n==max(.$n)) %>%
.$county_fips
df_us <- df_us %>% filter(county_fips %in% fips_complete)
Explore data
Plot distributions
# distribution of observations per county
df_us %>% group_by(county_fips) %>%
summarise(mark = mean(mark)) %>%
ggplot(aes(x=mark)) +
geom_histogram(color="black", fill="white", binwidth = 300) +
ggtitle('Distribution of observations per county')

# distributions of mean prevalence rates per county
df_us %>% group_by(county_fips) %>%
summarise(rate_day = mean(rate_day)) %>%
ggplot(aes(x=rate_day)) +
geom_histogram(color="black", fill="white", binwidth = 0.01) +
ggtitle('Distribution of mean prevalence rates by county')

# distribution of mean sd distance measue
df_us %>% group_by(county_fips) %>%
summarise(socdist_tiles = mean(socdist_tiles)) %>%
ggplot(aes(x=socdist_tiles)) +
geom_histogram(color="black", fill="white", bins = 200) +
ggtitle('Distribution of mean tiles visited measure by county')

# distribution of mean sd visit measue
df_us %>% group_by(county_fips) %>%
summarise(socdist_single_tile = mean(socdist_single_tile)) %>%
ggplot(aes(x=socdist_single_tile)) +
geom_histogram(color="black", fill="white", bins = 200) +
ggtitle('Distribution of mean single tile measute by county')

NA
NA
Plot prevalence over time
df_us %>% sample_n(20000) %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall prevalence over time")
pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_us %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}
Plot social distancing single tile visited
df_us %>% sample_n(10000) %>%
ggplot(aes(x=time, y=socdist_single_tile)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_us %>% mutate(dist_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(dist_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_single_tile)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~dist_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





Control for weekend effect
df_us %>% sample_n(10000) %>%
ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_us %>% mutate(dist_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(dist_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=county_name, size=mark)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~dist_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





Variance over time

Correlations
df_us %>% select(-time, -date, -county_name) %>%
group_by(county_fips) %>%
summarize_if(is.numeric, mean) %>%
select(-county_fips) %>%
cor(use='pairwise.complete.obs') %>%
round(3)
mark rate_day pers_o pers_c pers_e pers_a pers_n socdist_tiles socdist_single_tile
mark 1.000 0.189 0.279 -0.052 0.097 -0.041 -0.174 -0.377 0.214
rate_day 0.189 1.000 0.196 -0.049 0.044 -0.037 -0.094 -0.240 0.167
pers_o 0.279 0.196 1.000 -0.052 -0.086 -0.154 -0.228 -0.249 0.208
pers_c -0.052 -0.049 -0.052 1.000 0.148 0.650 -0.402 0.165 -0.258
pers_e 0.097 0.044 -0.086 0.148 1.000 0.235 -0.386 -0.065 -0.061
pers_a -0.041 -0.037 -0.154 0.650 0.235 1.000 -0.384 0.123 -0.277
pers_n -0.174 -0.094 -0.228 -0.402 -0.386 -0.384 1.000 0.062 0.190
socdist_tiles -0.377 -0.240 -0.249 0.165 -0.065 0.123 0.062 1.000 -0.595
socdist_single_tile 0.214 0.167 0.208 -0.258 -0.061 -0.277 0.190 -0.595 1.000
airport_distance -0.212 -0.055 -0.093 -0.094 -0.109 -0.103 0.040 0.258 -0.135
republican -0.345 -0.234 -0.349 -0.046 -0.077 -0.086 0.307 0.350 -0.231
medage -0.223 -0.075 -0.034 -0.066 -0.092 -0.074 0.232 0.013 0.301
male -0.115 -0.052 -0.117 -0.096 -0.058 -0.154 0.054 0.126 -0.041
popdens 0.322 0.375 0.222 -0.044 0.028 -0.070 -0.044 -0.244 0.221
manufact -0.162 -0.138 -0.386 0.070 0.034 0.119 0.179 0.057 -0.126
tourism 0.112 0.111 0.368 0.017 -0.003 -0.067 -0.188 -0.006 0.074
academics 0.418 0.284 0.462 -0.116 0.141 -0.145 -0.341 -0.434 0.259
medinc 0.307 0.228 0.226 -0.173 0.134 -0.219 -0.215 -0.444 0.225
physician_pc -0.181 -0.100 -0.213 0.107 -0.047 0.112 0.117 0.163 -0.114
airport_distance republican medage male popdens manufact tourism academics medinc
mark -0.212 -0.345 -0.223 -0.115 0.322 -0.162 0.112 0.418 0.307
rate_day -0.055 -0.234 -0.075 -0.052 0.375 -0.138 0.111 0.284 0.228
pers_o -0.093 -0.349 -0.034 -0.117 0.222 -0.386 0.368 0.462 0.226
pers_c -0.094 -0.046 -0.066 -0.096 -0.044 0.070 0.017 -0.116 -0.173
pers_e -0.109 -0.077 -0.092 -0.058 0.028 0.034 -0.003 0.141 0.134
pers_a -0.103 -0.086 -0.074 -0.154 -0.070 0.119 -0.067 -0.145 -0.219
pers_n 0.040 0.307 0.232 0.054 -0.044 0.179 -0.188 -0.341 -0.215
socdist_tiles 0.258 0.350 0.013 0.126 -0.244 0.057 -0.006 -0.434 -0.444
socdist_single_tile -0.135 -0.231 0.301 -0.041 0.221 -0.126 0.074 0.259 0.225
airport_distance 1.000 0.121 0.029 0.194 -0.144 -0.138 0.101 -0.133 -0.177
republican 0.121 1.000 0.134 0.162 -0.264 0.172 -0.220 -0.452 -0.192
medage 0.029 0.134 1.000 -0.040 -0.105 0.091 -0.080 -0.210 -0.107
male 0.194 0.162 -0.040 1.000 -0.101 -0.080 -0.046 -0.175 -0.004
popdens -0.144 -0.264 -0.105 -0.101 1.000 -0.120 0.049 0.242 0.154
manufact -0.138 0.172 0.091 -0.080 -0.120 1.000 -0.380 -0.385 -0.179
tourism 0.101 -0.220 -0.080 -0.046 0.049 -0.380 1.000 0.279 -0.022
academics -0.133 -0.452 -0.210 -0.175 0.242 -0.385 0.279 1.000 0.719
medinc -0.177 -0.192 -0.107 -0.004 0.154 -0.179 -0.022 0.719 1.000
physician_pc -0.038 0.196 0.078 0.159 -0.079 0.157 -0.226 -0.369 -0.202
physician_pc
mark -0.181
rate_day -0.100
pers_o -0.213
pers_c 0.107
pers_e -0.047
pers_a 0.112
pers_n 0.117
socdist_tiles 0.163
socdist_single_tile -0.114
airport_distance -0.038
republican 0.196
medage 0.078
male 0.159
popdens -0.079
manufact 0.157
tourism -0.226
academics -0.369
medinc -0.202
physician_pc 1.000
Model building
Prepare functions
# function calculates all relevant models
run_models <- function(y, lvl1_x, lvl2_x, lvl2_id, data, ctrls=F){
# subset data
data = data %>%
dplyr::select(all_of(y), all_of(lvl1_x), all_of(lvl2_x), all_of(lvl2_id),
popdens, rate_day, all_of(y))
data = data %>%
dplyr::rename(y = all_of(y),
lvl1_x = all_of(lvl1_x),
lvl2_x = all_of(lvl2_x),
lvl2_id = all_of(lvl2_id)
)
# configure optimization procedure
ctrl_config <- lmeControl(opt = 'optim', maxIter = 100, msMaxIter = 100)
# baseline
baseline <- lme(fixed = y ~ 1, random = ~ 1 | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# random intercept fixed slope
random_intercept <- lme(fixed = y ~ lvl1_x + lvl2_x,
random = ~ 1 | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# random intercept random slope
random_slope <- lme(fixed = y ~ lvl1_x + lvl2_x,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction <- lme(fixed = y ~ lvl1_x * lvl2_x,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# create list with results
results <- list('baseline' = baseline,
"random_intercept" = random_intercept,
"random_slope" = random_slope,
"interaction" = interaction)
if (ctrls == 'dem' | ctrls == 'prev'){
# random intercept random slope
random_slope_ctrl_dem <- lme(fixed = y ~ lvl1_x + lvl2_x + popdens,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction_ctrl_main_dem <- lme(fixed = y ~ lvl1_x * lvl2_x + popdens,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction_ctrl_int_dem <- lme(fixed = y ~ lvl1_x * lvl2_x + lvl1_x * popdens,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# create list with results
results <- list('baseline' = baseline,
"random_intercept" = random_intercept,
"random_slope" = random_slope,
"interaction" = interaction,
"random_slope_ctrl_dem" = random_slope_ctrl_dem,
"interaction_ctrl_main_dem" = interaction_ctrl_main_dem,
"interaction_ctrl_int_dem" = interaction_ctrl_int_dem)
}
if (ctrls == 'prev'){
# random intercept random slope
random_slope_ctrl_prev <- lme(fixed = y ~ lvl1_x + lvl2_x + popdens + rate_day,
random = ~ lvl1_x + rate_day | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction_ctrl_main_prev <- lme(fixed = y ~ lvl1_x * lvl2_x + popdens + rate_day,
random = ~ lvl1_x | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction_ctrl_int_prev<- lme(fixed = y ~ lvl1_x * lvl2_x + lvl1_x * popdens + rate_day,
random = ~ lvl1_x + rate_day | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# create list with results
results <- list('baseline' = baseline,
"random_intercept" = random_intercept,
"random_slope" = random_slope,
"interaction" = interaction,
"random_slope_ctrl_dem" = random_slope_ctrl_dem,
"interaction_ctrl_main_dem" = interaction_ctrl_main_dem,
"interaction_ctrl_int_dem" = interaction_ctrl_int_dem,
"random_slope_ctrl_prev" = random_slope_ctrl_prev,
"interaction_ctrl_main_prev" = interaction_ctrl_main_prev,
"interaction_ctrl_int_prev" = interaction_ctrl_int_prev)
}
if(ctrls == 'exp'){
# random intercept random slope
random_slope_exp <- lme(fixed = y ~ (lvl1_x + I(lvl1_x^2)) + lvl2_x,
random = ~ (lvl1_x + I(lvl1_x^2)) | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# cross level interaction
interaction_exp <- lme(fixed = y ~ (lvl1_x + I(lvl1_x^2)) * lvl2_x,
random = ~ (lvl1_x + I(lvl1_x^2)) | lvl2_id,
data = data,
correlation = corAR1(),
control = ctrl_config,
method = 'ML')
# create list with results
results <- list('baseline' = baseline,
"random_intercept" = random_intercept,
"random_slope" = random_slope,
"interaction" = interaction,
"random_slope_exp" = random_slope_exp,
"interaction_exp" = interaction_exp)
}
return(results)
}
# extracts table with coefficients and tests statistics
extract_results <- function(models) {
models_summary <- models %>%
map(summary) %>%
map("tTable") %>%
map(as.data.frame) %>%
map(round, 10)
# %>% map(~ .[str_detect(rownames(.), 'Inter|lvl'),])
return(models_summary)
}
compare_models <- function(models) {
mdl_names <- models %>% names()
str = ''
for (i in mdl_names){
mdl_str <- paste('models$', i, sep = '')
if(i == 'baseline'){
str <- mdl_str
}else{
str <- paste(str, mdl_str, sep=', ')
}
}
anova_str <- paste0('anova(', str, ')')
mdl_comp <- eval(parse(text=anova_str))
rownames(mdl_comp) = mdl_names
return(mdl_comp)
}
Predict prevalence
prevalence ~ openness
models_o_covid <-run_models(y = 'rate_day',
lvl1_x = 'time',
lvl2_x = 'pers_o',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'dem')
extract_results(models_o_covid)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
compare_models(models_o_covid)
NA
prevalence ~ conscientiousness
models_c_covid <-run_models(y = 'rate_day',
lvl1_x = 'time',
lvl2_x = 'pers_c',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'dem')
extract_results(models_c_covid)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
compare_models(models_c_covid)
NA
NA
prevalence ~ agreeableness
models_a_covid <-run_models(y = 'rate_day',
lvl1_x = 'time',
lvl2_x = 'pers_a',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'dem')
extract_results(models_a_covid)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
compare_models(models_a_covid)
NA
NA
prevalence ~ neuroticism
models_n_covid <-run_models(y = 'rate_day',
lvl1_x = 'time',
lvl2_x = 'pers_n',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'dem')
extract_results(models_n_covid)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
compare_models(models_n_covid)
NA
NA
Predict social distancing
social distancing ~ openness
models_o_sd <-run_models(y = 'socdist_single_tile',
lvl1_x = 'time',
lvl2_x = 'pers_o',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'prev')
extract_results(models_o_sd)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
$random_slope_ctrl_prev
$interaction_ctrl_main_prev
$interaction_ctrl_int_prev
compare_models(models_o_sd)
NA
NA
social distancing ~ conscientiousness
models_c_sd <-run_models(y = 'socdist_single_tile',
lvl1_x = 'time',
lvl2_x = 'pers_c',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'prev')
extract_results(models_c_sd)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
$random_slope_ctrl_prev
$interaction_ctrl_main_prev
$interaction_ctrl_int_prev
compare_models(models_c_sd)
NA
NA
social distancing ~ agreeableness
models_a_sd <-run_models(y = 'socdist_single_tile',
lvl1_x = 'time',
lvl2_x = 'pers_a',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'prev')
extract_results(models_a_sd)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
$random_slope_ctrl_prev
$interaction_ctrl_main_prev
$interaction_ctrl_int_prev
compare_models(models_a_sd)
NA
NA
social distancing ~ neuroticism
models_n_sd <-run_models(y = 'socdist_single_tile',
lvl1_x = 'time',
lvl2_x = 'pers_n',
lvl2_id = 'county_fips',
data = df_us_scaled,
ctrls = 'prev')
extract_results(models_n_sd)
$baseline
$random_intercept
$random_slope
$interaction
$random_slope_ctrl_dem
$interaction_ctrl_main_dem
$interaction_ctrl_int_dem
$random_slope_ctrl_prev
$interaction_ctrl_main_prev
$interaction_ctrl_int_prev
compare_models(models_n_sd)
NA
Create overview table
Define function to create overview tables
summary_table <- function(models, dv_name, prev=F){
temp_df_ctrl_main <- NULL
temp_df_ctrl_int <- NULL
temp_df_ctrl_int_prev <- NULL
for (i in models){
results <- i %>% extract_results()
results_ctrl_main <- results$interaction_ctrl_main_dem['lvl1_x:lvl2_x',]
temp_df_ctrl_main <- temp_df_ctrl_main %>% rbind(results_ctrl_main)
results_ctrl_int <- results$interaction_ctrl_int_dem['lvl1_x:lvl2_x',]
temp_df_ctrl_int <- temp_df_ctrl_int %>% rbind(results_ctrl_int)
if(prev){
results_ctrl_int_prev <- results$interaction_ctrl_int_prev['lvl1_x:lvl2_x',]
temp_df_ctrl_int_prev <- temp_df_ctrl_int_prev %>% rbind(results_ctrl_int_prev)
}
}
names_ctrl_main <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens')
rownames(temp_df_ctrl_main) <- names_ctrl_main
names_ctrl_int <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens*time')
rownames(temp_df_ctrl_int) <- names_ctrl_int
if(prev){
names_ctrl_int_prev <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens*time_prev')
rownames(temp_df_ctrl_int_prev) <- names_ctrl_int_prev
sum_tab <- rbind(temp_df_ctrl_main, temp_df_ctrl_int, temp_df_ctrl_int_prev) %>% round(4)
}else{
sum_tab <- rbind(temp_df_ctrl_main, temp_df_ctrl_int) %>% round(4)
}
return(sum_tab)
}
Create overview tables
# prevalence
models_prev <- list(models_o_covid,
models_c_covid,
models_e_covid,
models_a_covid,
models_n_covid)
sum_tab_prev <- summary_table(models_prev, dv_name = 'prev')
write.table(sum_tab_prev, quote=F)
Value Std.Error DF t-value p-value
prev~o*time_crtl_popdens 0.0244 0.0027 53135 9.1918 0
prev~c*time_crtl_popdens -0.0061 0.0027 53135 -2.266 0.0235
prev~e*time_crtl_popdens 0.0055 0.0027 53135 2.0302 0.0423
prev~a*time_crtl_popdens -0.0042 0.0027 53135 -1.572 0.116
prev~n*time_crtl_popdens -0.0109 0.0027 53135 -4.0489 1e-04
prev~o*time_crtl_popdens*time 0.013 0.0025 53134 5.1452 0
prev~c*time_crtl_popdens*time -0.0037 0.0025 53134 -1.5004 0.1335
prev~e*time_crtl_popdens*time 0.004 0.0025 53134 1.6065 0.1082
prev~a*time_crtl_popdens*time -4e-04 0.0025 53134 -0.179 0.858
prev~n*time_crtl_popdens*time -0.0085 0.0025 53134 -3.4502 6e-04
# social distancing
models_socdist <- list(models_o_sd,
models_c_sd,
models_e_sd,
models_a_sd,
models_n_sd)
sum_tab_socdist <- summary_table(models_socdist, dv_name = 'socdist', prev=T)
write.table(sum_tab_socdist, quote=F)
Value Std.Error DF t-value p-value
socdist~o*time_crtl_popdens 0.012 7e-04 53135 18.0098 0
socdist~c*time_crtl_popdens -0.006 7e-04 53135 -8.6385 0
socdist~e*time_crtl_popdens 0.0025 7e-04 53135 3.5181 4e-04
socdist~a*time_crtl_popdens -0.0067 7e-04 53135 -9.7575 0
socdist~n*time_crtl_popdens -0.0033 7e-04 53135 -4.5664 0
socdist~o*time_crtl_popdens*time 0.0099 7e-04 53134 14.8225 0
socdist~c*time_crtl_popdens*time -0.0055 7e-04 53134 -8.2817 0
socdist~e*time_crtl_popdens*time 0.0021 7e-04 53134 3.2025 0.0014
socdist~a*time_crtl_popdens*time -0.006 7e-04 53134 -9.0111 0
socdist~n*time_crtl_popdens*time -0.0028 7e-04 53134 -4.0934 0
socdist~o*time_crtl_popdens*time_prev 0.0102 7e-04 53133 15.0292 0
socdist~c*time_crtl_popdens*time_prev -0.0054 7e-04 53133 -8.1668 0
socdist~e*time_crtl_popdens*time_prev 0.0022 7e-04 53133 3.2724 0.0011
socdist~a*time_crtl_popdens*time_prev -0.0059 7e-04 53133 -8.8441 0
socdist~n*time_crtl_popdens*time_prev -0.0031 7e-04 53133 -4.5123 0
Conditional random forest analysis
Explore distribution of slopes
df_us_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram(bins = 100)

df_us_slope_socdist %>% ggplot(aes(slope_socdist)) + geom_histogram(bins = 100)

CRF prevalence ~ openness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_o_fit_prev <- cforest(slope_prev ~ pers_o + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_prev[-1],
controls = ctrls)
crf_o_varimp_prev <- varimp(crf_o_fit_prev, nperm = 1)
crf_o_varimp_cond_prev <- varimp(crf_o_fit_prev, conditional = T, nperm = 1)
crf_o_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_o_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF prevalence ~ conscientiousness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_c_fit_prev <- cforest(slope_prev ~ pers_c + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_prev[-1],
controls = ctrls)
crf_c_varimp_prev <- varimp(crf_c_fit_prev, nperm = 1)
crf_c_varimp_cond_prev <- varimp(crf_c_fit_prev, conditional = T, nperm = 1)
crf_c_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_c_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF prevalence ~ agreeableness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_a_fit_prev <- cforest(slope_prev ~ pers_a + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_prev[-1],
controls = ctrls)
crf_a_varimp_prev <- varimp(crf_a_fit_prev, nperm = 1)
crf_a_varimp_cond_prev <- varimp(crf_a_fit_prev, conditional = T, nperm = 1)
crf_a_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_a_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF prevalence ~ neuroticism
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_n_fit_prev <- cforest(slope_prev ~ pers_n + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_prev[-1],
controls = ctrls)
crf_n_varimp_prev <- varimp(crf_n_fit_prev, nperm = 1)
crf_n_varimp_cond_prev <- varimp(crf_n_fit_prev, conditional = T, nperm = 1)
crf_n_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_n_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF social distancing ~ openness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_o_fit_socdist <- cforest(slope_socdist ~ pers_o + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_socdist[-1],
controls = ctrls)
crf_o_varimp_socdist <- varimp(crf_o_fit_socdist, nperm = 1)
crf_o_varimp_cond_socdist <- varimp(crf_o_fit_socdist, conditional = T, nperm = 1)
crf_o_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_o_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF social distancing ~ conscientiousness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_c_fit_socdist <- cforest(slope_socdist ~ pers_c + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_socdist[-1],
controls = ctrls)
crf_c_varimp_socdist <- varimp(crf_c_fit_socdist, nperm = 1)
crf_c_varimp_cond_socdist <- varimp(crf_c_fit_socdist, conditional = T, nperm = 1)
crf_c_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_c_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF social distancing ~ agreeableness
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_a_fit_socdist <- cforest(slope_socdist ~ pers_a + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_socdist[-1],
controls = ctrls)
crf_a_varimp_socdist <- varimp(crf_a_fit_socdist, nperm = 1)
crf_a_varimp_cond_socdist <- varimp(crf_a_fit_socdist, conditional = T, nperm = 1)
crf_a_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_a_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

CRF social distancing ~ neuroticism
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_n_fit_socdist <- cforest(slope_socdist ~ pers_n + airport_distance + republican +
medage + male + popdens + manufact +
tourism + academics + medinc + physician_pc,
df_us_slope_socdist[-1],
controls = ctrls)
crf_n_varimp_socdist <- varimp(crf_n_fit_socdist, nperm = 1)
crf_n_varimp_cond_socdist <- varimp(crf_n_fit_socdist, conditional = T, nperm = 1)
crf_n_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_n_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Linear models predicting slopes from personality
lm_slope_prev_pers <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_us_slope_prev)
lm_slope_prev_pers %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_us_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-0.11019 -0.02930 -0.01466 0.00224 1.64943
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.027854 0.001886 14.769 < 2e-16 ***
pers_o 0.017433 0.002045 8.525 < 2e-16 ***
pers_c -0.006911 0.002553 -2.707 0.00683 **
pers_e 0.004472 0.002094 2.135 0.03282 *
pers_a 0.000545 0.002601 0.210 0.83404
pers_n -0.005611 0.002374 -2.363 0.01819 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.09289 on 2420 degrees of freedom
Multiple R-squared: 0.04663, Adjusted R-squared: 0.04466
F-statistic: 23.67 on 5 and 2420 DF, p-value: < 2.2e-16
lm_slope_socdist_pers <- lm(slope_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_us_slope_socdist)
lm_slope_socdist_pers %>% summary()
Call:
lm(formula = slope_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_us_slope_socdist)
Residuals:
Min 1Q Median 3Q Max
-0.28392 -0.02426 -0.00637 0.01813 1.65694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.104604 0.001132 92.426 < 2e-16 ***
pers_o 0.008687 0.001227 7.079 1.89e-12 ***
pers_c -0.004476 0.001532 -2.922 0.003512 **
pers_e 0.003246 0.001257 2.583 0.009841 **
pers_a -0.003405 0.001561 -2.181 0.029245 *
pers_n -0.004956 0.001425 -3.479 0.000513 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05574 on 2420 degrees of freedom
Multiple R-squared: 0.04961, Adjusted R-squared: 0.04764
F-statistic: 25.26 on 5 and 2420 DF, p-value: < 2.2e-16
Linear models predicting slopes with controls
lm_slope_prev_pers <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n +
airport_distance + republican + medage + male + popdens +
manufact + tourism + academics + medinc + physician_pc,
data = df_us_slope_prev)
lm_slope_prev_pers %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_distance + republican + medage + male +
popdens + manufact + tourism + academics + medinc + physician_pc,
data = df_us_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-0.31430 -0.02291 -0.00994 0.00277 1.59045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.790e-02 1.713e-03 16.285 < 2e-16 ***
pers_o 2.761e-03 2.187e-03 1.263 0.206874
pers_c -3.284e-03 2.360e-03 -1.391 0.164245
pers_e 1.138e-03 1.932e-03 0.589 0.555932
pers_a 5.501e-03 2.476e-03 2.221 0.026416 *
pers_n 2.122e-03 2.359e-03 0.900 0.368348
airport_distance 2.754e-03 1.865e-03 1.477 0.139782
republican -7.602e-03 2.085e-03 -3.646 0.000272 ***
medage -6.650e-05 1.807e-03 -0.037 0.970654
male -2.818e-05 1.876e-03 -0.015 0.988018
popdens 3.369e-02 1.832e-03 18.395 < 2e-16 ***
manufact -2.120e-03 2.059e-03 -1.030 0.303341
tourism 5.024e-03 2.081e-03 2.414 0.015849 *
academics 4.209e-03 3.415e-03 1.232 0.217913
medinc 1.237e-02 2.824e-03 4.379 1.24e-05 ***
physician_pc 6.768e-04 1.889e-03 0.358 0.720186
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08439 on 2410 degrees of freedom
Multiple R-squared: 0.2164, Adjusted R-squared: 0.2115
F-statistic: 44.37 on 15 and 2410 DF, p-value: < 2.2e-16
lm_slope_socdist_pers <- lm(slope_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n +
airport_distance + republican + medage + male + popdens +
manufact + tourism + academics + medinc + physician_pc,
data = df_us_slope_socdist)
lm_slope_socdist_pers %>% summary()
Call:
lm(formula = slope_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_distance + republican + medage + male +
popdens + manufact + tourism + academics + medinc + physician_pc,
data = df_us_slope_socdist)
Residuals:
Min 1Q Median 3Q Max
-0.26752 -0.01730 -0.00324 0.01248 1.63123
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1046597 0.0010362 101.000 < 2e-16 ***
pers_o -0.0003449 0.0013227 -0.261 0.794320
pers_c -0.0010022 0.0014272 -0.702 0.482618
pers_e 0.0004776 0.0011681 0.409 0.682670
pers_a 0.0007837 0.0014976 0.523 0.600800
pers_n 0.0023991 0.0014266 1.682 0.092753 .
airport_distance -0.0037722 0.0011277 -3.345 0.000835 ***
republican -0.0109897 0.0012610 -8.715 < 2e-16 ***
medage 0.0058495 0.0010931 5.351 9.55e-08 ***
male 0.0053010 0.0011348 4.671 3.16e-06 ***
popdens 0.0055870 0.0011077 5.044 4.91e-07 ***
manufact 0.0023425 0.0012451 1.881 0.060040 .
tourism 0.0039623 0.0012585 3.148 0.001662 **
academics 0.0056901 0.0020653 2.755 0.005912 **
medinc 0.0131087 0.0017081 7.675 2.39e-14 ***
physician_pc -0.0029367 0.0011424 -2.570 0.010215 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05104 on 2410 degrees of freedom
Multiple R-squared: 0.2067, Adjusted R-squared: 0.2017
F-statistic: 41.85 on 15 and 2410 DF, p-value: < 2.2e-16
gg <- gg + geom_map(data=us_map_shape, map=us_map_shape,
aes(x = x, y = y, map_id=fips),
color="black", fill="white", size=0.25)
Error: `map` must have the columns `x`, `y`, and `id`
df_us_covid %>% filter(county == 23013)
Change point analysis
library(changepoint)
Successfully loaded changepoint package version 2.2.2
NOTE: Predefined penalty values changed in version 2.2. Previous penalty values with a postfix 1 i.e. SIC1 are now without i.e. SIC and previous penalties without a postfix i.e. SIC are now with a postfix 0 i.e. SIC0. See NEWS and help files for further details.
Preparation
# keep only counties with full data
fips_complete <- df_us_scaled %>%
group_by(county_fips) %>%
summarize(n = n()) %>%
filter(n==max(.$n)) %>%
.$county_fips
Prevalence
# run changepoint analysis
df_us_prev_cpt_results <- df_us_scaled %>% select(county_fips, rate_day) %>%
filter(county_fips %in% fips_complete) %>%
split(.$county_fips) %>%
map(~ cpt.meanvar(as.vector(.$rate_day),
class=TRUE,
param.estimates=TRUE,
Q=1))
# calculate change point
df_us_prev_cpt_day <- df_us_prev_cpt_results %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_prev = '.') %>%
rownames_to_column('county_fips')
# calculate mean differences
df_us_prev_cpt_mean_diff <- df_us_prev_cpt_results %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_prev = '.') %>%
rownames_to_column('county_fips')
# calculate varaince differences
df_us_prev_cpt_var_diff <- df_us_prev_cpt_results %>%
map(param.est) %>%
map(~ .$variance) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(var_diff_prev = '.') %>%
rownames_to_column('county_fips')
# merge new variables
df_us_cpt_prev <- df_us_scaled %>%
select(-time, -rate_day, -socdist_single_tile, -socdist_single_tile_clean) %>%
distinct() %>%
mutate(county_fips = as.character(county_fips)) %>%
left_join(df_us_prev_cpt_day, by='county_fips') %>%
left_join(df_us_prev_cpt_mean_diff, by='county_fips') %>%
left_join(df_us_prev_cpt_var_diff, by='county_fips')
df_us_cpt_prev %>% select(cpt_day_prev) %>% map(hist)
$cpt_day_prev
$breaks
[1] 2 4 6 8 10 12 14 16 18 20 22
$counts
[1] 10 32 148 4 189 674 9 7 10 78
$density
[1] 0.004306632 0.013781223 0.063738157 0.001722653 0.081395349 0.290267011 0.003875969 0.003014643
[9] 0.004306632 0.033591731
$mids
[1] 3 5 7 9 11 13 15 17 19 21
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev %>% select(mean_diff_prev) %>% map(hist)
$mean_diff_prev
$breaks
[1] 0 2 4 6 8 10 12 14 16 18 20 22 24 26
$counts
[1] 1106 34 7 5 2 3 3 0 0 0 0 0 1
$density
[1] 0.4763135228 0.0146425495 0.0030146425 0.0021533161 0.0008613264 0.0012919897 0.0012919897
[8] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004306632
$mids
[1] 1 3 5 7 9 11 13 15 17 19 21 23 25
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev %>% select(var_diff_prev) %>% map(hist)
$var_diff_prev
$breaks
[1] -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
$counts
[1] 59 1007 4 0 3 4 6 0 0 0 0 0 0 0 0 1
$density
[1] 2.721402e-03 4.644834e-02 1.845018e-04 0.000000e+00 1.383764e-04 1.845018e-04 2.767528e-04
[8] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
[15] 0.000000e+00 4.612546e-05
$mids
[1] -10 10 30 50 70 90 110 130 150 170 190 210 230 250 270 290
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev %>% dim()
[1] 2397 19
df_us_cpt_prev %>% drop_na() %>% dim()
[1] 1080 19
for(i in head(df_us_prev_cpt_results,18)){
plot(i)
}

















NA

Socdist
# run changepoint analysis
df_us_socdist_cpt_results <- df_us_scaled %>% select(county_fips, socdist_single_tile_clean) %>%
filter(county_fips %in% fips_complete) %>%
split(.$county_fips) %>%
map(~ cpt.meanvar(as.vector(.$socdist_single_tile_clean),
#penalty = 'Asymptotic',
class=TRUE,
param.estimates=TRUE,
Q=1,
test.stat = 'Normal'))
# calculate change point
df_us_socdist_cpt_day <- df_us_socdist_cpt_results %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_socdist = '.') %>%
rownames_to_column('county_fips')
# calculate mean differences
df_us_socdist_cpt_mean_diff <- df_us_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_socdist = '.') %>%
rownames_to_column('county_fips')
# calculate varaince differences
df_us_socdist_cpt_var_diff <- df_us_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$variance) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(var_diff_socdist = '.') %>%
rownames_to_column('county_fips')
# merge new variables
df_us_cpt_prev_socdist <- df_us_cpt_prev %>%
left_join(df_us_socdist_cpt_day, by='county_fips') %>%
left_join(df_us_socdist_cpt_mean_diff, by='county_fips') %>%
left_join(df_us_socdist_cpt_var_diff, by='county_fips')
df_us_cpt_prev_socdist %>% select(cpt_day_socdist) %>% map(hist)
$cpt_day_socdist
$breaks
[1] 2 4 6 8 10 12 14 16 18 20 22
$counts
[1] 11 116 564 592 591 76 106 12 2 327
$density
[1] 0.0022945348 0.0241969128 0.1176470588 0.1234876929 0.1232790989 0.0158531498 0.0221109720
[8] 0.0025031289 0.0004171882 0.0682102628
$mids
[1] 3 5 7 9 11 13 15 17 19 21
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev_socdist %>% select(mean_diff_socdist) %>% map(hist)
$mean_diff_socdist
$breaks
[1] -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
$counts
[1] 6 42 337 1043 667 236 53 8 3 1 1
$density
[1] 0.0050062578 0.0350438048 0.2811848144 0.8702544848 0.5565289946 0.1969128077 0.0442219441
[8] 0.0066750104 0.0025031289 0.0008343763 0.0008343763
$mids
[1] -0.25 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev_socdist %>% select(var_diff_socdist) %>% map(hist)
$var_diff_socdist
$breaks
[1] -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
$counts
[1] 2 4 10 24 77 171 757 762 163 52 14 22 10 1 2 1
$density
[1] 0.004826255 0.009652510 0.024131274 0.057915058 0.185810811 0.412644788 1.826737452 1.838803089
[9] 0.393339768 0.125482625 0.033783784 0.053088803 0.024131274 0.002413127 0.004826255 0.002413127
$mids
[1] -1.3 -1.1 -0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7
$xname
[1] ".x[[i]]"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

df_us_cpt_prev_socdist %>% dim()
[1] 2397 22
df_us_cpt_prev_socdist %>% drop_na() %>% dim()
[1] 999 22
for(i in head(df_us_socdist_cpt_results,18)){
plot(i)
}

















NA

Predicting change points
Linear models predicting change points (no controls)
lm_cpt_socdist_pers %>% summary()
Call:
lm(formula = cpt_day_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_us_cpt_prev_socdist)
Residuals:
Min 1Q Median 3Q Max
-9.284 -3.119 -1.269 1.149 12.870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.48853 0.09662 118.902 < 2e-16 ***
pers_o -0.26056 0.10496 -2.483 0.013112 *
pers_c 0.15915 0.13093 1.216 0.224291
pers_e -0.35704 0.10756 -3.319 0.000915 ***
pers_a 0.61317 0.13367 4.587 4.72e-06 ***
pers_n 0.14504 0.12217 1.187 0.235243
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.731 on 2391 degrees of freedom
Multiple R-squared: 0.02744, Adjusted R-squared: 0.02541
F-statistic: 13.49 on 5 and 2391 DF, p-value: 5.281e-13
Linear models predicting change points with controls
lm_cpt_socdist_pers %>% summary()
Call:
lm(formula = cpt_day_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + airport_distance + republican + medage + male +
popdens + manufact + tourism + academics + medinc + physician_pc,
data = df_us_cpt_prev_socdist)
Residuals:
Min 1Q Median 3Q Max
-9.784 -3.111 -1.220 1.563 12.148
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.476908 0.094787 121.080 < 2e-16 ***
pers_o 0.083785 0.121134 0.692 0.48921
pers_c -0.008695 0.130595 -0.067 0.94692
pers_e -0.256051 0.107138 -2.390 0.01693 *
pers_a 0.415986 0.137692 3.021 0.00255 **
pers_n -0.314164 0.131217 -2.394 0.01673 *
airport_distance 0.077067 0.102536 0.752 0.45236
republican 0.533550 0.115417 4.623 3.99e-06 ***
medage 0.156947 0.100341 1.564 0.11792
male 0.017010 0.104308 0.163 0.87047
popdens 0.158159 0.101480 1.559 0.11924
manufact 0.077820 0.114000 0.683 0.49490
tourism -0.280650 0.114817 -2.444 0.01458 *
academics 0.066161 0.189260 0.350 0.72669
medinc -0.873767 0.156588 -5.580 2.68e-08 ***
physician_pc 0.139214 0.104699 1.330 0.18376
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.636 on 2376 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.07064, Adjusted R-squared: 0.06477
F-statistic: 12.04 on 15 and 2376 DF, p-value: < 2.2e-16
---
title: "COVID-19 US"
author: "Heinrich Peters"
date: "4/15/2020"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

# MAC
 knitr::opts_knit$set(root.dir = '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/US')

library(lmerTest)
library(nlme)
library(psych)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(party)
library(doParallel)

```


# Prepare county level data 

### Overview of time windows
US prevalence: 03/09 - 04/18
US socdist: 03/01 - 05/03

UK prevalence: 03/09 - 04/10
UK socdist: 03/01 - 03/31

GER prevalence: 01/01 - 04/25
GER socdist: 02/25 - 04/27

### Read and format prevalence data 
```{r, warning=FALSE, message=FALSE}

df_us_covid <- read_csv('timeseries_usa_county_march1_april_09.csv')
df_us_covid$time %>% max()

df_us_covid <- df_us_covid %>% 
  filter(time <=31) %>% 
  arrange(countyfips) %>%
  mutate(stabil = -stabil) %>%
  dplyr::rename(county_fips = countyfips,
         pers_o = open, 
         pers_c = sci,
         pers_e = extra,
         pers_a = agree,
         pers_n = stabil)


df_us_covid <- df_us_covid %>% 
  dplyr::select(county_fips, time, mark, rate_day, pers_o,
                pers_c, pers_e, pers_a, pers_n)


df_us_covid %>% head()
```

### Conty level controls 
```{r}

df_us_ctrl <- read.csv('controls_US.csv')

df_us_ctrl <- df_us_ctrl %>% select(-county_name) %>% 
  rename(county_fips = county)

df_us_ctrl %>% head()

```


### Social distancing data unacast
```{r, warning=FALSE, message=FALSE}

df_us_socdist <- read_csv('0409_sds-full-county.csv')

# create sequence of dates
date_sequence <- seq.Date(as.Date('2020-03-09'),
                          as.Date('2020-03-31'), 1)
                     

# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge day index with gps data
df_us_socdist = df_us_socdist %>% 
  merge(df_dates, by='date') %>% 
  arrange(county_fips) %>%
  as_tibble()

df_us_socdist %>% head()
```


### Social distancing data FB
```{r, warning=FALSE, message=FALSE}

fb_files <- list.files('../FB Data/US individual files/Mobility/',
                       '*.csv', full.names = T)

df_us_socdist_fb <- fb_files %>% 
  map(read_csv) %>% bind_rows()

df_us_socdist_fb$ds %>% summary()

df_us_socdist_fb <- df_us_socdist_fb %>%
  select(-age_bracket, -gender, -baseline_name, -baseline_type) %>%
  rename(date = ds,
         county_fips = polygon_id,
         county_name = polygon_name,
         socdist_tiles = all_day_bing_tiles_visited_relative_change,
         socdist_single_tile = all_day_ratio_single_tile_users)

df_us_socdist_fb <- df_us_socdist_fb %>%
  filter(date >= '2020-03-09' & date <= '2020-03-31') %>%
  group_by(county_fips) %>% 
  arrange(date) %>% 
  mutate(time = row_number()) %>%
  ungroup() %>% 
  arrange(county_fips)

head(df_us_socdist_fb)
```

### Sanity check socdist data
```{r}
socdist <- df_us_socdist %>% merge(df_us_socdist_fb, by = c("county_fips", "time")) 

socdist[c('daily_distance_diff', 'daily_visitation_diff', 'socdist_tiles', 'socdist_single_tile')] %>% 
  cor(use = 'pairwise.complete')

```


### Merge data
```{r}

df_us <- plyr::join_all(list(df_us_covid, df_us_socdist_fb),
                  by = c('county_fips', 'time'), 
                  type = 'inner') %>% 
  plyr::join(df_us_ctrl, by='county_fips') %>% 
  arrange(county_fips, time)

# keep only counties with full data
fips_complete <- df_us %>% 
  group_by(county_fips) %>% 
  summarize(n = n()) %>% 
  filter(n==max(.$n)) %>% 
  .$county_fips

df_us <- df_us %>% filter(county_fips %in% fips_complete)

```

## Explore data
### Plot distributions
```{r, warning=FALSE}

# distribution of observations per county
df_us %>% group_by(county_fips) %>% 
  summarise(mark = mean(mark)) %>% 
  ggplot(aes(x=mark)) + 
  geom_histogram(color="black", fill="white", binwidth = 300) +
  ggtitle('Distribution of observations per county')

  
# distributions of mean prevalence rates per county
df_us %>% group_by(county_fips) %>% 
  summarise(rate_day = mean(rate_day)) %>%
  ggplot(aes(x=rate_day)) + 
  geom_histogram(color="black", fill="white", binwidth = 0.01) +
  ggtitle('Distribution of mean prevalence rates by county')

  
# distribution of mean sd distance measue
df_us %>% group_by(county_fips) %>% 
  summarise(socdist_tiles = mean(socdist_tiles)) %>%
  ggplot(aes(x=socdist_tiles)) + 
  geom_histogram(color="black", fill="white", bins = 200) +
 ggtitle('Distribution of mean tiles visited measure by county')


# distribution of mean sd visit measue
df_us %>% group_by(county_fips) %>% 
  summarise(socdist_single_tile = mean(socdist_single_tile)) %>%
  ggplot(aes(x=socdist_single_tile)) + 
  geom_histogram(color="black", fill="white", bins = 200) +
  ggtitle('Distribution of mean single tile measute by county')


```

### Plot prevalence over time
```{r}

df_us %>% sample_n(20000) %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall prevalence over time")



pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_us %>% mutate(prev_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

### Plot social distancing single tile visited
```{r}

df_us %>% sample_n(10000) %>%
  ggplot(aes(x=time, y=socdist_single_tile)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_us %>% mutate(dist_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(dist_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_single_tile)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~dist_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

### Control for weekend effect 
```{r}

weekend <- c(6, 7, 13, 14, 20, 21)

df_us_loess <- df_us %>% filter(!time %in% weekend) %>% 
  split(.$county_fips) %>%
  map(~ loess(socdist_single_tile ~ time, data = .)) %>%
  map(predict, 1:23) %>% 
  bind_rows() %>% 
  gather(key = 'county_fips', value = 'loess') %>% 
  group_by(county_fips) %>% 
  mutate(time = row_number())

df_us <- df_us %>% merge(df_us_loess, by=c('county_fips', 'time')) %>% 
  mutate(socdist_single_tile_clean = ifelse(time %in% weekend, loess,
                                            socdist_single_tile)) %>%
  arrange(county_fips, time)

df_us

```

```{r}

df_us %>% sample_n(10000) %>%
  ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_us %>% mutate(dist_tail = cut(.[[i]], 
                                       breaks = c(-Inf, quantile(.[[i]], 0.05), quantile(.[[i]], 0.95), Inf),
                                       labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(dist_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=county_name, size=mark)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~dist_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```

### Variance over time
```{r}

df_us %>% group_by(time) %>% 
  summarize(socdist_var = var(socdist_single_tile)) %>% 
  ggplot(aes(x=time, y=socdist_var)) +
  geom_line() +
  ggtitle("Variance of social distancing index over time")


df_us %>% group_by(time) %>% 
  summarize(socdist_var = var(socdist_single_tile),
            socdist_var_clean = var(socdist_single_tile_clean)) %>% 
  ggplot() +
  #geom_line(aes(x=time, y=socdist_var)) +
  geom_line(aes(x=time, y=socdist_var_clean)) +
  ggtitle("Variance of smothed social distancing index over time")

```




### Correlations 

```{r}

df_us %>% select(-time, -date, -county_name) %>% 
  group_by(county_fips) %>%
  summarize_if(is.numeric, mean) %>% 
  select(-county_fips) %>%
  cor(use='pairwise.complete.obs') %>% 
  round(3)
  
```


# Model building

## Prepare functions

```{r}

# function calculates all relevant models
run_models <- function(y, lvl1_x, lvl2_x, lvl2_id, data, ctrls=F){

  # subset data
  data = data %>% 
    dplyr::select(all_of(y), all_of(lvl1_x), all_of(lvl2_x), all_of(lvl2_id), 
                  popdens, rate_day, all_of(y))
  data = data %>% 
    dplyr::rename(y = all_of(y),
           lvl1_x = all_of(lvl1_x),
           lvl2_x = all_of(lvl2_x),
           lvl2_id = all_of(lvl2_id)
           )
  
  # configure optimization procedure
  ctrl_config <- lmeControl(opt = 'optim', maxIter = 100, msMaxIter = 100)

  # baseline
  baseline <- lme(fixed = y ~ 1, random = ~ 1 | lvl2_id, 
                    data = data,
                    correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')

  # random intercept fixed slope
  random_intercept <- lme(fixed = y ~ lvl1_x + lvl2_x, 
                          random = ~ 1 | lvl2_id,
                            data = data,
                            correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')

  # random intercept random slope
  random_slope <- lme(fixed = y ~ lvl1_x + lvl2_x, 
                      random = ~ lvl1_x | lvl2_id, 
                        data = data,
                        correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')

  # cross level interaction
  interaction <- lme(fixed = y ~ lvl1_x * lvl2_x, 
                     random = ~ lvl1_x | lvl2_id, 
                       data = data,
                       correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')
  
  # create list with results
  results <- list('baseline' = baseline, 
                  "random_intercept" = random_intercept, 
                  "random_slope" = random_slope,
                  "interaction" = interaction)
  
  
  if (ctrls == 'dem' | ctrls == 'prev'){
    
    # random intercept random slope
    random_slope_ctrl_dem <- lme(fixed = y ~ lvl1_x + lvl2_x + popdens,
                              random = ~ lvl1_x | lvl2_id, 
                          data = data,
                          correlation = corAR1(),
                    control = ctrl_config,
                  method = 'ML')
  
    # cross level interaction
    interaction_ctrl_main_dem <- lme(fixed = y ~ lvl1_x * lvl2_x + popdens,
                             random = ~ lvl1_x | lvl2_id, 
                         data = data,
                         correlation = corAR1(),
                    control = ctrl_config,
                  method = 'ML')
  
    # cross level interaction
    interaction_ctrl_int_dem <- lme(fixed = y ~ lvl1_x * lvl2_x + lvl1_x * popdens,
                             random = ~ lvl1_x | lvl2_id, 
                         data = data,
                         correlation = corAR1(),
                    control = ctrl_config,
                  method = 'ML')        
    
    # create list with results
    results <- list('baseline' = baseline, 
                    "random_intercept" = random_intercept, 
                    "random_slope" = random_slope,
                    "interaction" = interaction,
                    "random_slope_ctrl_dem" = random_slope_ctrl_dem,
                    "interaction_ctrl_main_dem" = interaction_ctrl_main_dem,
                    "interaction_ctrl_int_dem" = interaction_ctrl_int_dem)
  }
  
  if (ctrls == 'prev'){
  
    # random intercept random slope
    random_slope_ctrl_prev <- lme(fixed = y ~ lvl1_x + lvl2_x + popdens + rate_day,
                              random = ~ lvl1_x + rate_day | lvl2_id, 
                          data = data,
                          correlation = corAR1(),
                          control = ctrl_config,
                  method = 'ML')  
    
        # cross level interaction
    interaction_ctrl_main_prev <- lme(fixed = y ~ lvl1_x * lvl2_x + popdens + rate_day,
                             random = ~ lvl1_x | lvl2_id, 
                         data = data,
                         correlation = corAR1(),
                    control = ctrl_config,
                  method = 'ML')
  
  
    # cross level interaction
    interaction_ctrl_int_prev<- lme(fixed = y ~ lvl1_x * lvl2_x + lvl1_x * popdens + rate_day,
                             random = ~ lvl1_x + rate_day | lvl2_id, 
                         data = data,
                         correlation = corAR1(),
                          control = ctrl_config,
                  method = 'ML')
  
    # create list with results
    results <- list('baseline' = baseline, 
                    "random_intercept" = random_intercept, 
                    "random_slope" = random_slope,
                    "interaction" = interaction,
                    "random_slope_ctrl_dem" = random_slope_ctrl_dem,
                    "interaction_ctrl_main_dem" = interaction_ctrl_main_dem,
                    "interaction_ctrl_int_dem" = interaction_ctrl_int_dem,                    
                    "random_slope_ctrl_prev" = random_slope_ctrl_prev,
                    "interaction_ctrl_main_prev" = interaction_ctrl_main_prev,
                    "interaction_ctrl_int_prev" = interaction_ctrl_int_prev)
  }
  
  if(ctrls == 'exp'){
    # random intercept random slope
  random_slope_exp <- lme(fixed = y ~ (lvl1_x + I(lvl1_x^2)) + lvl2_x, 
                      random = ~ (lvl1_x + I(lvl1_x^2)) | lvl2_id, 
                        data = data,
                        correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')

  # cross level interaction
  interaction_exp <- lme(fixed = y ~ (lvl1_x + I(lvl1_x^2)) * lvl2_x, 
                     random = ~ (lvl1_x + I(lvl1_x^2)) | lvl2_id, 
                       data = data,
                       correlation = corAR1(),
                  control = ctrl_config,
                  method = 'ML')  
  
  
  # create list with results
  results <- list('baseline' = baseline, 
                  "random_intercept" = random_intercept, 
                  "random_slope" = random_slope,
                  "interaction" = interaction,                  
                  "random_slope_exp" = random_slope_exp,
                  "interaction_exp" = interaction_exp)
  }
  
  return(results)
        
}

# extracts table with coefficients and tests statistics
extract_results <- function(models) {
  
  models_summary <- models %>% 
  map(summary) %>% 
  map("tTable") %>% 
  map(as.data.frame) %>% 
  map(round, 10) 
  # %>% map(~ .[str_detect(rownames(.), 'Inter|lvl'),])
  
  return(models_summary)
  
}


compare_models <- function(models) {

  mdl_names <- models %>% names()
  
  str = ''
  for (i in mdl_names){
    
    mdl_str <- paste('models$', i, sep = '')
    
    if(i == 'baseline'){
      str <- mdl_str
    }else{
    str <- paste(str, mdl_str, sep=', ')
    }
  }
  
  anova_str <- paste0('anova(', str, ')')
  mdl_comp <- eval(parse(text=anova_str))
  rownames(mdl_comp) = mdl_names
  return(mdl_comp)
}

```

## Rescale Data
```{r}

lvl2_scaled <- df_us %>% 
  select(-time, -mark, -date, -county_name, -rate_day,
         -socdist_tiles, -socdist_single_tile, -socdist_single_tile_clean, -loess) %>% 
  distinct() %>% 
  mutate_at(vars(-county_fips), scale)

lvl1_scaled <- df_us %>% 
  select(county_fips, time, rate_day, socdist_single_tile, socdist_single_tile_clean) %>% 
  mutate_at(vars(-county_fips, -time), scale)

df_us_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'county_fips')

df_us_scaled
```


## Predict prevalence
### prevalence ~ openness
```{r}

models_o_covid <-run_models(y = 'rate_day',
                         lvl1_x = 'time',
                         lvl2_x = 'pers_o',
                         lvl2_id = 'county_fips',
                         data = df_us_scaled,
                         ctrls = 'dem')

extract_results(models_o_covid)

compare_models(models_o_covid)

```

### prevalence ~ conscientiousness
```{r}

models_c_covid <-run_models(y = 'rate_day', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_c', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'dem')

extract_results(models_c_covid)

compare_models(models_c_covid)


```

### prevalence ~ extraversion
```{r}

models_e_covid <-run_models(y = 'rate_day', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_e', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'dem')

extract_results(models_e_covid)

compare_models(models_e_covid)


```

### prevalence ~ agreeableness
```{r}

models_a_covid <-run_models(y = 'rate_day', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_a', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'dem')

extract_results(models_a_covid)

compare_models(models_a_covid)


```

### prevalence ~ neuroticism
```{r}

models_n_covid <-run_models(y = 'rate_day', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_n', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'dem')

extract_results(models_n_covid)

compare_models(models_n_covid)


```


## Predict social distancing
### social distancing ~ openness
```{r}

models_o_sd <-run_models(y = 'socdist_single_tile', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_o', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'prev')

extract_results(models_o_sd)

compare_models(models_o_sd)


```

### social distancing ~ conscientiousness
```{r}

models_c_sd <-run_models(y = 'socdist_single_tile', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_c', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'prev')

extract_results(models_c_sd)

compare_models(models_c_sd)


```

### social distancing ~ extraversion
```{r}

models_e_sd <-run_models(y = 'socdist_single_tile', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_e', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'prev')

extract_results(models_e_sd)

compare_models(models_e_sd)


```

### social distancing ~ agreeableness
```{r}

models_a_sd <-run_models(y = 'socdist_single_tile', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_a', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'prev')

extract_results(models_a_sd)

compare_models(models_a_sd)


```

### social distancing ~ neuroticism
```{r}

models_n_sd <-run_models(y = 'socdist_single_tile', 
                         lvl1_x = 'time', 
                         lvl2_x = 'pers_n', 
                         lvl2_id = 'county_fips', 
                         data = df_us_scaled,
                         ctrls = 'prev')

extract_results(models_n_sd)

compare_models(models_n_sd)

```


## Create overview table 

### Define function to create overview tables
```{r}

summary_table <- function(models, dv_name, prev=F){

  temp_df_ctrl_main <- NULL
  temp_df_ctrl_int <- NULL
  temp_df_ctrl_int_prev <- NULL
  
  for (i in models){
    results <- i %>% extract_results()
    
    results_ctrl_main <- results$interaction_ctrl_main_dem['lvl1_x:lvl2_x',]
    temp_df_ctrl_main <- temp_df_ctrl_main %>% rbind(results_ctrl_main)
    
    results_ctrl_int <- results$interaction_ctrl_int_dem['lvl1_x:lvl2_x',]
    temp_df_ctrl_int <- temp_df_ctrl_int %>% rbind(results_ctrl_int)
    
    if(prev){
      results_ctrl_int_prev <- results$interaction_ctrl_int_prev['lvl1_x:lvl2_x',]
      temp_df_ctrl_int_prev <- temp_df_ctrl_int_prev %>% rbind(results_ctrl_int_prev)
    }
        
  }
  
  names_ctrl_main <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens')
  rownames(temp_df_ctrl_main) <- names_ctrl_main

  names_ctrl_int <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens*time')
  rownames(temp_df_ctrl_int) <- names_ctrl_int

  if(prev){
    names_ctrl_int_prev <- paste0(dv_name, '~', c('o', 'c', 'e', 'a', 'n'), '*time', '_crtl_popdens*time_prev')
    rownames(temp_df_ctrl_int_prev) <- names_ctrl_int_prev
    
    sum_tab <- rbind(temp_df_ctrl_main, temp_df_ctrl_int, temp_df_ctrl_int_prev) %>% round(4)
  }else{
    sum_tab <- rbind(temp_df_ctrl_main, temp_df_ctrl_int) %>% round(4)
  }


  
  return(sum_tab)

} 

```

### Create overview tables
```{r}
# prevalence
models_prev <- list(models_o_covid, 
                    models_c_covid, 
                    models_e_covid, 
                    models_a_covid, 
                    models_n_covid)

sum_tab_prev <- summary_table(models_prev, dv_name = 'prev')

write.table(sum_tab_prev, quote=F)

# social distancing
models_socdist <- list(models_o_sd, 
                       models_c_sd, 
                       models_e_sd, 
                       models_a_sd, 
                       models_n_sd)

sum_tab_socdist <- summary_table(models_socdist, dv_name = 'socdist', prev=T)

write.table(sum_tab_socdist, quote=F)


```




# Conditional random forest analysis 

### Extract slopes
```{r}

# slope prevalence
df_us_slope_prev <- df_us_scaled %>% split(.$county) %>% 
  map(~ lm(rate_day ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('county_fips') %>% 
  rename(slope_prev = '.')

df_us_slope_prev <- df_us_scaled %>% select(-time, -rate_day, -socdist_single_tile) %>%
  distinct() %>% 
  mutate(county_fips = as.character(county_fips)) %>%
  inner_join(df_us_slope_prev, by = 'county_fips') %>%
  drop_na()



# slope social distancing
df_us_slope_socdist <- df_us_scaled %>% split(.$county) %>% 
  map(~ lm(socdist_single_tile ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('county_fips') %>% 
  rename(slope_socdist = '.')

df_us_slope_socdist <- df_us_scaled %>% select(-time, -rate_day, -socdist_single_tile) %>%
  distinct() %>% 
  mutate(county_fips = as.character(county_fips)) %>%
  inner_join(df_us_slope_socdist, by = 'county_fips') %>%
  drop_na()

df_us_slope_socdist
```

### Explore distribution of slopes
```{r}
df_us_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram(bins = 100)

df_us_slope_socdist %>% ggplot(aes(slope_socdist)) + geom_histogram(bins = 100)

```

# CRF prevalence ~ openness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_o_fit_prev <- cforest(slope_prev ~ pers_o + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_prev[-1], 
                         controls = ctrls)

crf_o_varimp_prev <- varimp(crf_o_fit_prev, nperm = 1)
crf_o_varimp_cond_prev <- varimp(crf_o_fit_prev, conditional = T, nperm = 1)

crf_o_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_o_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

# CRF prevalence ~ conscientiousness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_c_fit_prev <- cforest(slope_prev ~ pers_c + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_prev[-1], 
                         controls = ctrls)

crf_c_varimp_prev <- varimp(crf_c_fit_prev, nperm = 1)
crf_c_varimp_cond_prev <- varimp(crf_c_fit_prev, conditional = T, nperm = 1)

crf_c_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_c_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```


# CRF prevalence ~ extraversion
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_e_fit_prev <- cforest(slope_prev ~ pers_e + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_prev[-1], 
                         controls = ctrls)

crf_e_varimp_prev <- varimp(crf_e_fit_prev, nperm = 1)
crf_e_varimp_cond_prev <- varimp(crf_e_fit_prev, conditional = T, nperm = 1)

crf_e_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_e_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```


# CRF prevalence ~ agreeableness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_a_fit_prev <- cforest(slope_prev ~ pers_a + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_prev[-1], 
                         controls = ctrls)

crf_a_varimp_prev <- varimp(crf_a_fit_prev, nperm = 1)
crf_a_varimp_cond_prev <- varimp(crf_a_fit_prev, conditional = T, nperm = 1)

crf_a_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_a_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```


# CRF prevalence ~ neuroticism
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_n_fit_prev <- cforest(slope_prev ~ pers_n + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_prev[-1], 
                         controls = ctrls)

crf_n_varimp_prev <- varimp(crf_n_fit_prev, nperm = 1)
crf_n_varimp_cond_prev <- varimp(crf_n_fit_prev, conditional = T, nperm = 1)

crf_n_varimp_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_n_varimp_cond_prev %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```


# CRF social distancing ~ openness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_o_fit_socdist <- cforest(slope_socdist ~ pers_o + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_socdist[-1], 
                         controls = ctrls)

crf_o_varimp_socdist <- varimp(crf_o_fit_socdist, nperm = 1)
crf_o_varimp_cond_socdist <- varimp(crf_o_fit_socdist, conditional = T, nperm = 1)

crf_o_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_o_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```

# CRF social distancing ~ conscientiousness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_c_fit_socdist <- cforest(slope_socdist ~ pers_c + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_socdist[-1], 
                         controls = ctrls)

crf_c_varimp_socdist <- varimp(crf_c_fit_socdist, nperm = 1)
crf_c_varimp_cond_socdist <- varimp(crf_c_fit_socdist, conditional = T, nperm = 1)

crf_c_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_c_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```

# CRF social distancing ~ extraversion
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_e_fit_socdist <- cforest(slope_socdist ~ pers_e + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_socdist[-1], 
                         controls = ctrls)

crf_e_varimp_socdist <- varimp(crf_e_fit_socdist, nperm = 1)
crf_e_varimp_cond_socdist <- varimp(crf_e_fit_socdist, conditional = T, nperm = 1)

crf_e_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_e_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```

# CRF social distancing ~ agreeableness
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_a_fit_socdist <- cforest(slope_socdist ~ pers_a + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_socdist[-1], 
                         controls = ctrls)

crf_a_varimp_socdist <- varimp(crf_a_fit_socdist, nperm = 1)
crf_a_varimp_cond_socdist <- varimp(crf_a_fit_socdist, conditional = T, nperm = 1)

crf_a_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_a_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```


# CRF social distancing ~ neuroticism
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_n_fit_socdist <- cforest(slope_socdist ~ pers_n + airport_distance + republican +
                          medage + male + popdens + manufact +
                          tourism + academics + medinc + physician_pc, 
                         df_us_slope_socdist[-1], 
                         controls = ctrls)

crf_n_varimp_socdist <- varimp(crf_n_fit_socdist, nperm = 1)
crf_n_varimp_cond_socdist <- varimp(crf_n_fit_socdist, conditional = T, nperm = 1)

crf_n_varimp_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_n_varimp_cond_socdist %>% as.data.frame() %>% rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

```

# Linear models predicting slopes from personality
```{r}

lm_slope_prev_pers <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_us_slope_prev)
lm_slope_prev_pers %>% summary()


lm_slope_socdist_pers <- lm(slope_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                            data = df_us_slope_socdist)
lm_slope_socdist_pers %>% summary()

```

# Linear models predicting slopes with controls
```{r}

lm_slope_prev_pers <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n + 
                           airport_distance + republican + medage + male + popdens + 
                           manufact + tourism + academics + medinc + physician_pc,
                         data = df_us_slope_prev)
lm_slope_prev_pers %>% summary()


lm_slope_socdist_pers <- lm(slope_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n + 
                              airport_distance + republican + medage + male + popdens + 
                              manufact + tourism + academics + medinc + physician_pc,
                            data = df_us_slope_socdist)
lm_slope_socdist_pers %>% summary()

```


```{r}
library(tigris)
library(ggplot2)
library(ggthemes)

me <- counties(cb = TRUE)
me_map <- fortify(me)
plot(me_map)

me_map %>% head()
me %>% head()

gg <- ggplot()
gg <- gg + geom_map(data=us_map_shape, map=us_map_shape,
                    aes( x = x, y = y, map_id=fips),
                    color="black", fill="white", size=0.25)
gg <- gg + coord_map()
gg <- gg + theme_map()
gg
```

```{r}
df_us_covid %>% filter(county == 23013)

```


```{r}

library(usmap)
library(ggplot2)

us_map_shape = us_map(regions = 'counties')

us_map_shape

plot_usmap(data = us_map_shape) + 
  labs(title = "US Counties",
       subtitle = "This is a blank map of the counties of the United States.") + 
  theme(panel.background = element_rect(color = "black", fill = "lightblue"))
```

# Change point analysis

```{r}
library(changepoint)
```

### Preparation
```{r}
# keep only counties with full data
fips_complete <- df_us_scaled %>% 
  group_by(county_fips) %>% 
  summarize(n = n()) %>% 
  filter(n==max(.$n)) %>% 
  .$county_fips
```


```{r}
df_us_scaled
```


### Prevalence
```{r}

# run changepoint analysis
df_us_prev_cpt_results <- df_us_scaled %>% select(county_fips, rate_day) %>%
  filter(county_fips %in% fips_complete) %>% 
  split(.$county_fips) %>%
  map(~ cpt.meanvar(as.vector(.$rate_day),
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1))

# calculate change point
df_us_prev_cpt_day <- df_us_prev_cpt_results %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_prev = '.') %>%
  rownames_to_column('county_fips')

# calculate mean differences
df_us_prev_cpt_mean_diff <- df_us_prev_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_prev = '.') %>%
  rownames_to_column('county_fips')

# calculate varaince differences
df_us_prev_cpt_var_diff <- df_us_prev_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$variance) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(var_diff_prev = '.') %>%
  rownames_to_column('county_fips')

# merge new variables 
df_us_cpt_prev <- df_us_scaled %>%
  select(-time, -rate_day, -socdist_single_tile, -socdist_single_tile_clean) %>%
  distinct() %>%
  mutate(county_fips = as.character(county_fips)) %>%
  left_join(df_us_prev_cpt_day, by='county_fips') %>%
  left_join(df_us_prev_cpt_mean_diff, by='county_fips') %>%
  left_join(df_us_prev_cpt_var_diff, by='county_fips')

df_us_cpt_prev %>% select(cpt_day_prev) %>% map(hist)
df_us_cpt_prev %>% select(mean_diff_prev) %>% map(hist)
df_us_cpt_prev %>% select(var_diff_prev) %>% map(hist)

df_us_cpt_prev %>% dim()
df_us_cpt_prev %>% drop_na() %>% dim()

```


```{r}

for(i in head(df_us_prev_cpt_results,18)){
  plot(i)
}

```

### Socdist
```{r}

# run changepoint analysis
df_us_socdist_cpt_results <- df_us_scaled %>% select(county_fips, socdist_single_tile_clean) %>%
  filter(county_fips %in% fips_complete) %>% 
  split(.$county_fips) %>%
  map(~ cpt.meanvar(as.vector(.$socdist_single_tile_clean),
                    #penalty = 'Asymptotic',
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1,
                    test.stat = 'Normal'))

# calculate change point
df_us_socdist_cpt_day <- df_us_socdist_cpt_results %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_socdist = '.') %>%
  rownames_to_column('county_fips')

# calculate mean differences
df_us_socdist_cpt_mean_diff <- df_us_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_socdist = '.') %>%
  rownames_to_column('county_fips')

# calculate varaince differences
df_us_socdist_cpt_var_diff <- df_us_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$variance) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(var_diff_socdist = '.') %>%
  rownames_to_column('county_fips')

# merge new variables 
df_us_cpt_prev_socdist <- df_us_cpt_prev %>%
  left_join(df_us_socdist_cpt_day, by='county_fips') %>%
  left_join(df_us_socdist_cpt_mean_diff, by='county_fips') %>%
  left_join(df_us_socdist_cpt_var_diff, by='county_fips')

df_us_cpt_prev_socdist %>% select(cpt_day_socdist) %>% map(hist)
df_us_cpt_prev_socdist %>% select(mean_diff_socdist) %>% map(hist)
df_us_cpt_prev_socdist %>% select(var_diff_socdist) %>% map(hist)

df_us_cpt_prev_socdist %>% dim()
df_us_cpt_prev_socdist %>% drop_na() %>% dim()

```

```{r}

for(i in head(df_us_socdist_cpt_results,18)){
  plot(i)
}

```
```{r}
df_us_cpt_prev_socdist
```

# Predicting change points 
### Linear models predicting change points (no controls)
```{r}

lm_cpr_prev_pers <- lm(cpt_day_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_us_cpt_prev_socdist)
lm_cpr_prev_pers %>% summary()


lm_cpt_socdist_pers <- lm(cpt_day_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                            data = df_us_cpt_prev_socdist)
lm_cpt_socdist_pers %>% summary()

```

# Linear models predicting change points with controls
```{r}

lm_cpt_prev_pers <- lm(cpt_day_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n + 
                           airport_distance + republican + medage + male + popdens + 
                           manufact + tourism + academics + medinc + physician_pc,
                         data = df_us_cpt_prev_socdist)
lm_cpt_prev_pers %>% summary()

lm_cpt_socdist_pers <- lm(cpt_day_socdist ~ pers_o + pers_c + pers_e + pers_a + pers_n + 
                              airport_distance + republican + medage + male + popdens + 
                              manufact + tourism + academics + medinc + physician_pc,
                            data = df_us_cpt_prev_socdist)
lm_cpt_socdist_pers %>% summary()

```



Social distancing data unacast